Krystsina Liaukovich
Biographic Data
| ID | 7971329 |
|---|---|
| NAME | Krystsina Liaukovich |
| GIVEN NAMES | Krystsina |
| FAMILY NAME | Liaukovich |
| SIGNATURE | LIAUKOVICH K |
| AFFILIATIONS | Institute of Higher Nervous Activity and Neurophysiology |
| ORCID | 0000-0002-1016-2703 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Commentary
The narrative review by Zaboski et al. (1) provides a timely and valuable synthesis of the application of deep learning (DL) to obsessive-compulsive disorder (OCD). The authors effectively argue for the unique potential of DL to handle the marked heterogeneity and complexity of OCD, which often eludes traditional statistical methods. By focusing exclusively on OCD, the review offers a necessary and nuanced examination that broader psychiatric AI …
The Effect of Experimental Conditions, the Sample Size and Session Duration on Resting-State Subjective Experience
Our research aims to study the difference in resting-state during EEG and fMRI experimental conditions, each of which has its own peculiarities. Therefore, we conducted EEG and fMRI experiments very close in terms of conditions, during which 108 and 109 healthy participants, respectively, were recorded at rest for ten minutes. Subjective content of conscious during resting-state was assessed using the Amsterdam Resting-State Questionnaire (ARSQ) …
No prominent works on this page.
The Effect of Experimental Conditions, the Sample Size and Session Duration on Resting-State Subjective Experience
Our research aims to study the difference in resting-state during EEG and fMRI experimental conditions, each of which has its own peculiarities. Therefore, we conducted EEG and fMRI experiments very close in terms of conditions, during which 108 and 109 healthy participants, respectively, were recorded at rest for ten minutes. Subjective content of conscious during resting-state was assessed using the Amsterdam Resting-State Questionnaire (ARSQ) …
Commentary
The narrative review by Zaboski et al. (1) provides a timely and valuable synthesis of the application of deep learning (DL) to obsessive-compulsive disorder (OCD). The authors effectively argue for the unique potential of DL to handle the marked heterogeneity and complexity of OCD, which often eludes traditional statistical methods. By focusing exclusively on OCD, the review offers a necessary and nuanced examination that broader psychiatric AI …
Audiology (1 work) · Big data (1 work) · Confirmatory factor analysis (1 work) · Data curation (1 work) · Deep learning (1 work) · Digital Mental Health Interventions (1 work) · EEG and Brain-Computer Interfaces (1 work) · Electroencephalography (1 work) · Feeling (1 work) · Field (mathematics (1 work)